Genome-wide association study adjusting for familial relatedness identifies novel loci for food intake in the UK Biobank

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Abstract This study aimed to identify genetic risk loci associated with dietary intake using recently revealed data of over 93 million variants from the UK Biobank. By adjusting for familial relatedness among individuals in a linear mixed model, we identified a total of 399 genomic risk loci for the consumption of red meat (n = 15), processed meat (n = 12), poultry (n = 1), total fish (n = 28), milk (n = 50), cheese (n = 59), total fruits (n = 82), total vegetables (n = 50), coffee (n = 33), tea (n = 40), and alcohol (n = 57). Of these, 13 variants in previous study did not reach suggestive significant level (p = 1.0e-5). Under the LDAK model, the heritability (h2) was highest for the consumption of cheese (h2 = 10.48%), alcohol (h2 = 9.71%) and milk (h2 = 9.01%), followed by tea (h2 = 8.34%) and fruits (h2 = 7.83%). Of these, the highest genetic correlation (r2) was observed between milk and tea consumption (r2 = 0.86). Post-GWA analyses were further conducted to identify variant annotations and functional pathways using summary statistics. Overall, by analyzing the updated data with adjustment for familial relatedness in this large-scale database, we identified several novel loci for food intake. Further investigations in other populations are needed to understand the contribution of genetic factors to dietary habits in populations of various ethnic backgrounds.

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